OrgDyn

OrgDyn analyzes time-series 2D organoid contours to quantify and model temporal morphological dynamics for studying developmental and disease-related shape changes.

Key Features:

• Feature- and Model-Based Approaches: Combines feature extraction and modeling of 2D organoid contours to capture geometric and dynamic behaviors.
• Geometrical and Signal Processing Feature Extraction: Extracts geometrical and signal-processing features from organoid contour images.
• Dimensionality Reduction: Applies dimensionality reduction techniques to simplify complex feature spaces and distinguish dynamical paths.
• Time Series Clustering: Clusters time-series data to identify groups of organoids with similar dynamic behaviors.
• Dynamical Modeling Using Point Distribution Models: Uses point distribution models to explain and predict temporal shape variations.

Scientific Applications:

• High-throughput organoid experiments: Enables quantitative analysis of organoid systems that mimic features of mammalian tissues in high-throughput experiments.
• Studies of tissue development and disease: Supports investigation of the molecular basis of tissue development and disease by quantifying morphological dynamics.
• Characterization of dynamical paths: Characterizes diverse dynamical paths leading to different final shapes in organoids.
• Comparative clustering of organoids: Clusters organoids based on their dynamic behaviors to facilitate comparative studies.
• Modeling temporal shape variation: Models temporal shape variations to understand developmental trajectories or pathological changes.

Methodology:

Extract geometrical and signal-processing features from 2D organoid contours, apply dimensionality reduction, perform time-series clustering, and fit point distribution models to explain temporal shape variations.